A conventional geologic model, as used in the oil and gas industry, is a computer-based representation of a subsurface earth volume, such as a petroleum reservoir or a depositional basin. Technology for three-dimensional (hereinafter “3-dimensional” or 3D) geological modeling or static reservoir modeling continues to advance.
Seismic-to-simulation is the process of generating three-dimensional models of hydrocarbon reservoirs for predicting production, selecting well placement, and optimizing reservoir management. The resulting three-dimensional model should faithfully represent original well logs, seismic data, and production history.
Building the three-dimensional grid is the most difficult part of the seismic-to-simulation workflow. The process is extremely expensive in terms of user time and is considered to be an art. Moreover, the corner-point grid contains intrinsic limitations in terms of the degree of structural complexity that can be reproduced.
The upstream oil and gas industry thus struggles with creating efficient grids and the tools to work with them. The following disadvantages of constructing the theoretical grids are evident:                Building a three-dimensional grid requires an initial step consisting of a large amount of manual work. All of the subsequent steps are dependent upon this initial manual stage.        Typical grids have up to 50 million cells. In a standard workflow a single iteration requires that values be calculated for all of the cells. This may require between ten seconds and ten hours depending on the grid size and the algorithm being used.        In the three-dimensional grid, the space coordinates I, J, K are integer values, so everything can appear chunky. X and Y values are converted to I and J values as though the grid cells were perfectly square. They are not, so property distribution is very dependent upon the grid shape.        Many of the new versions of the three-dimensional grid are optimized for simulation rather than property modeling. This introduces quite serious limitations in terms of complexity of faulting.        In addition, developments in seismic interpretation, such as automatic fault extraction and cluster technology, and developments in simulation, such as parallelization and use of unstructured grids, can overload the modeling step.        A standard three-dimensional grid-based modeling exercise requires several iterations. In a single iteration, the user must specify parameters for the property propagation, while the process calculates values for each cell in the model, potentially tens of millions. A quality check must be performed on the result by looking at layers and cross sections, adjusting the parameters and potentially recalculating. If the three-dimensional grid is changed, then the model needs to be recreated. Such iteration may take several minutes and even then only a small percentage of the millions of cells in which the property has been calculated are displayed during the step of quality checking.        Corner-point grids always require a compromise of true fidelity to the actual geological structure. Structural framework models contain only geological horizons and faults and not subdivision into cells, and so are not compromised to the same extent as corner-point grids. Ideally, property population would be done on the structural framework.        
What is needed is way to derive the benefits of a corner-point grid model from a structural framework model, without having to compute the corner-point grid.